Although computer systems can store a wealth of information, it can often be difficult for users to find specific information or effectively explore a particular subject area of interest. A variety of search engines currently exist that allow users to search for information by entering a search query comprising one or more keywords that may be of interest to the user. After receiving a search request from a user, a search engine identifies documents and/or web pages that are relevant based on the keywords. Often, the search engine returns a large number of documents or web page addresses, and the user is then left to sift through the list of documents, links, and associated information to find desired information. This process can be cumbersome, frustrating, and time consuming for the user.
A variety of techniques have been employed by search engines in an attempt to assist users in navigating search results and finding relevant documents. One approach is to provide a table of contents (TOC) that includes a list of topics relevant to the search query. A user may select a topic from the TOC and view search results relevant to the selected topic. In some implementations, the TOC remains static as the user selects different topics from the TOC, allowing the user to navigate to different sets of search results within the context of the original search query.
Often, TOCs are manually generated by search engine personnel. In particular, search engine personnel identify top-end queries (i.e., the search queries having the greatest search volumes for the search engine) and manually identify the topics relevant to each search query. However, this approach is very labor intensive, and it would be impractical to manually generate TOCs for torso and tail-end queries (i.e., the search queries having lower search volumes for the search engine). In some instances, a TOC may be algorithmically determined for a search query, for instance, by identifying a domain to which the search query pertains (e.g., auto, finance, etc.) and providing a TOC based on the domain. However, this approach may be ineffective for some search queries. As a result, the TOC provided for some search queries (e.g., torso and tail-end queries) is either non-existent or of very poor quality. This leads to an inconsistent experience for search users.